Biology subjects

Oudiette, D.

Publications and source records attributed to Oudiette, D..

5 recordsLinked to original sources

bioRxiv2025

Live access to the emotional dynamics of REM sleep dreams in lucid dreamers with narcolepsy

Sleep helps regulate emotions, but it is still unclear whether -and how- the emotions we experience in dreams contribute to this regulation. To uncover the potential function of dream emotions, we must first understand what they are and how they unfold in dreams. The emotional content of dreams has mostly been studied using post-sleep dream reports, which provide a biased and static snapshot of a complex and dynamic experience. In this study, we took a more direct approach, accessing dream emotions in real-time. We asked twenty-four lucid dreamers with narcolepsy to report the emotional valence of their dreams, - positive, negative or neutral-, while still asleep, using predefined facial codes during daytime naps monitored with polysomnography. Of the 126 naps recorded, 62 contained at least one emotional code during REM sleep, yielding 191 codes in total. The ratios of positive and negative codes were evenly balanced per nap. The 33 naps with at least two codes allowed us to track the dream emotional dynamics. Over half of these naps showed opposite emotional valences (positive and negative). By measuring the time elapsed between codes, we estimated the average duration of a given dreams emotional valence in REM sleep to be about one minute. Positive emotions emerged on average earlier than negative ones during lucid REM sleep. These findings confirm the highly emotional nature of dreams and, more importantly, highlight that emotions in REM sleep dreams are fluid and fast-changing. Such emotional dynamics during REM sleep dreams may help us to better understand the mechanisms of the emotional regulatory function of dreams.

neuroscience↗

bioRxiv2025

EEG Signature of Idiopathic Hypersomnia: Insights from Sleep Microarchitecture and Hypnodensity Metrics

Background and ObjectivesPatients with idiopathic hypersomnia with long sleep time (IH) report daytime hypersomnolence despite prolonged sleep time and normal sleep macrostructure. As they often have non-restorative sleep, we investigated whether the structure of their sleep is abnormal. MethodsIn polysomnography recordings from 80 IH participants and 48 controls, we quantified hypnodensity metrics across the night (macro level), periodic and aperiodic spectral properties, infraslow fluctuations of sigma power within the night (meso level), slow waves, sleep spindles and their clustering (microstructure). Multivariate machine-learning models were used to classify IH vs. control sleep. ResultsHypnodensity metrics were comparable between IH and controls, apart from more mixed wake/N1 sleep epochs in IH, and greater divergence between consecutive epochs of the same stage during NREM sleep in IH. Sigma power was increased in N2 sleep in IH and sleep spindles were more frequent and clustered. Slow wave density was higher in IH. Higher mean spindle cluster size correlated with higher Epworth Sleepiness Scale scores. Multivariate machine learning models incorporating these features achieved a balanced accuracy of 74% in distinguishing IH from controls. DiscussionWhile spindles and slow waves are typically associated with good sleep quality, they are increased in IH patients. This could reflect greater need for sleep and increased difficulty waking up in IH, which is also characterized by more mixed wake/N1 stages.

neuroscience↗

bioRxiv2025

How sleeping minds decide: state-specific reconfigurations of lexical decision-making

Decision-making is a core cognitive function that enables adaptive behavior across diverse contexts. While extensively studied in wakefulness, its persistence and reconfiguration across sleep states remain poorly understood. Here, we use computational modeling to examine lexical decision-making across wakefulness, N1 sleep, and lucid REM sleep in both healthy participants (HP) and participants with narcolepsy (NP). Using facial electromyography (EMG) to capture real-time behavioral responses to spoken words and pseudowords during sleep, we quantify how decision-making strategies adapt under different sleep and consciousness states. Our findings reveal two key insights. First, decision-making mechanisms are dynamically reconfigured across sleep states. In N1 sleep, the advantage for word (vs. pseudoword) judgments is supported by faster sensory encoding and motor preparation, combined with efficient evidence accumulation. In contrast, in lucid REM sleep, the word advantage is driven exclusively by enhanced evidence accumulation, while sensory encoding and motor preparation remain unchanged. Second, cross-state comparisons reveal distinct patterns of preservation and impairment. In N1 sleep, word judgment remains largely intact, whereas pseudoword judgment is significantly impaired, characterized by prolonged stimulus encoding, delayed motor preparation, and reduced evidence accumulation. In contrast, lucid REM sleep is marked by a global reduction in processing efficiency, reflected in slower evidence accumulation and elevated decision thresholds for both words and pseudowords. These results demonstrate that rather than being uniformly degraded, decision-making is dynamically reconfigured across sleep stages, reflecting adaptive neurocognitive strategies that sustain cognition in altered states of consciousness. By identifying state-specific computational mechanisms, this study provides new insights into the brains resilience and flexibility under changing cognitive and physiological conditions.

neuroscience↗

bioRxiv2022

Behavioral and brain responses to verbal stimuli reveal transient periods of cognitive integration of external world in all sleep stages

Sleep has long been considered as a state of disconnection from the environment, with absent reactivity to external stimuli. Here, we questioned this sleep disconnection dogma by directly investigating behavioral responsiveness in 49 napping subjects (27 with narcolepsy and 22 healthy volunteers) engaged in a lexical decision task. Participants were instructed to frown or smile depending on the stimulus type (words vs pseudo-words). We found accurate behavioral responses, visible via contractions of the corrugator or zygomatic muscles, in all sleep stages in both groups (except slow-wave sleep for healthy volunteers). Stimuli presented during states with high (vs. low) values of neural markers indexing rich cognitive states more often yielded responses. Our findings suggest that transient windows of reactivity to external stimuli exist in all sleep stages, even in healthy individuals. Such windows of reactivity could be used to probe sleep-related mental and cognitive processes in real-time across all sleep stages.

neuroscience↗

bioRxiv2022

Memory loss at sleep onset

Every night, we pass through a transitory zone at the borderland between wakefulness and sleep, named the first stage of non-rapid eye movement sleep (N1). N1 sleep is associated with an increased hippocampal activity and dreamlike experiences that incorporate recent wake materials, suggesting that it may be associated with memory processing. Here, we investigated the specific contribution of N1 sleep in the processing of memory traces. Participants were asked to learn the precise locations of 48 objects on a grid and were then tested on their memory for these items before and after a 30-minute rest during which participants either stayed fully awake, transitioned toward N1 or deeper (N2) sleep. We showed that memory recall was lower (10% forgetting) after a resting period including only N1 sleep compared to N2 sleep. Furthermore, the ratio of alpha/theta power (an EEG marker of the transition towards sleep) correlated negatively with the forgetting rate when taking into account all sleepers (N1 and N2 groups combined), suggesting a physiological index for memory loss that transcends sleep stages. Our findings suggest that interrupting sleep onset at N1 may alter sleep-dependent memory consolidation and promote forgetting.

neuroscience↗